Space-time adaptive processing (STAP) needs to estimate clutter covariance matrix by training sample data.However

this estimation is always corrupted by outliers

which even lead to target self-nulling phenomenon.Hence

a novel robust STAP algorithm based on joint sparse recovery of clutter spectrum is proposed

which can eliminate the influence of outlier.This algorithm is applied in side-looking airborne radar.When the sparse recovery is high resolution

the algorithm exploits the characteristic that distribution and correlation between clutter and outlier are different among multiple snapshots.The norm is employed to select the most suitable sparse recovery coefficients to estimate the clutter spectrum

so outlier can be eliminated effectively.Monte Carlo experiments prove that the proposed algorithm has advantages in robustness and target detection over other conventional STAP algorithms in non-homogeneous clutter environments.